1. Field of the Invention
The present invention relates to an HMM (Hidden Markov Model) learning device and method, a program, and a recording medium, and specifically relates to an HMM learning device and method, a program, and a recording medium whereby effective and stable learning can be performed at the time of performing autonomous learning under a changing environment.
2. Description of the Related Art
Employment of HMM (Hidden Markov Model) has been proposed as a method for handling a sensor signal observed from a system serving as an object as time series data, and learning this as a probability model having both of a state and a state transition. HMM is one technique widely used for audio recognition. HMM is a state transition model defined by a state transition probability, and an output probability density function in each state, and parameters thereof are estimated so as to maximize likelihood. The Baum-Welch algorithm has been widely employed as a parameter estimating method.
HMM is a model whereby transition can be made from each state to another state via a state transition probability, wherein modeling is performed as a process of which the state is changed. However, with HMM, usually, which state an observed sensor signal corresponds to is determined only in a probabilistic manner.
Therefore, the Viterbi algorithm has widely been employed as a method for determining a state transition process so as to obtain the highest likelihood based on an observed sensor signal. Thus, the state corresponding to the sensor signal at each point in time can uniquely be determined. Also, even though the same sensor signals are observed from a system in a different situation, the state transition processes thereof can be handled as different state transition processes according to difference of the temporal change processes of the sensor signals before and after each point in time. Though a perceptual aliasing problem is not completely solved, a different state may be assigned to the same sensor signals, and accordingly, the state of the system may be modeled in detail as compared to SOM or the like (e.g., see Lawrence R. Rabiner (February 1989), “A tutorial on Hidden Markov Models and selected application in speech recognition”, Proceedings of the IEEE 77(2): 257-286).